






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						KandinskyVaaPipeline
     UpperCamelCase_				:		List[Any]								=						[
         '''image_embeds''',
         '''negative_image_embeds''',
     ]
     UpperCamelCase_				:		Tuple								=						['''image_embeds''', '''negative_image_embeds''']
     UpperCamelCase_				:		Any								=						[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCamelCase_				:		List[str]								=						False


     @property
     def 					_A							(					self							:						List[Any]   ):
       return 32


     @property
     def 					_A							(					self							:						List[Any]   ):
       return 32


     @property
     def 					_A							(					self							:						Any   ):
       return self.time_input_dim


     @property
     def 					_A							(					self							:						Union[str, Any]   ):
       return self.time_input_dim * 4


     @property
     def 					_A							(					self							:						Tuple   ):
       return 100


     @property
     def 					_A							(					self							:						Optional[int]   ):
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE							:   int						    =     {
           "in_channels": 4,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       SCREAMING_SNAKE_CASE							:   str						    =     UNetaDConditionModel(**UpperCAmelCase_   )
       return model


     @property
     def 					_A							(					self							:						int   ):
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }


     @property
     def 					_A							(					self							:						Any   ):
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     VQModel(**self.dummy_movq_kwargs   )
       return model


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.dummy_unet
       SCREAMING_SNAKE_CASE							:   str						    =     self.dummy_movq

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     DDIMScheduler(
           num_train_timesteps=1000       ,			beta_schedule="linear"       ,			beta_start=0.00_085       ,			beta_end=0.012       ,			clip_sample=UpperCAmelCase_       ,			set_alpha_to_one=UpperCAmelCase_       ,			steps_offset=1       ,			prediction_type="epsilon"       ,			thresholding=UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[Any]=0   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(seed + 1   )   ).to(
           UpperCAmelCase_   )
       if str(UpperCAmelCase_   ).startswith("mps"   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.manual_seed(UpperCAmelCase_   )
       else:
         SCREAMING_SNAKE_CASE							:   Dict						    =     torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs






     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     "cpu"

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe(**self.get_dummy_inputs(UpperCAmelCase_   )   )
       SCREAMING_SNAKE_CASE							:   str						    =     output.images

       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipe(
           **self.get_dummy_inputs(UpperCAmelCase_   )       ,			return_dict=UpperCAmelCase_       ,			)[0]

       SCREAMING_SNAKE_CASE							:   str						    =     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE							:   Any						    =     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     np.array(
           [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]   )

       assert (
           np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Any   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"   )

       SCREAMING_SNAKE_CASE							:   int						    =     KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"       ,			torch_dtype=torch.floataa   )
       pipe_prior.to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder"       ,			torch_dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipeline.to(UpperCAmelCase_   )
       pipeline.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "red cat, 4k photo"

       SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.Generator(device="cuda"   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     pipe_prior(
           UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			num_inference_steps=5       ,			negative_prompt=""       ,			).to_tuple()

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.Generator(device="cuda"   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipeline(
           image_embeds=UpperCAmelCase_       ,			negative_image_embeds=UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			num_inference_steps=100       ,			output_type="np"       ,			)

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(UpperCAmelCase_       ,			UpperCAmelCase_   )














"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE   =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')




@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Optional[int]      =  SpeechTaTokenizer
					lowercase_    :				Dict      =  False
					lowercase_    :				Any      =  True
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											super().setUp()

											# We have a SentencePiece fixture for testing
											lowerCAmelCase   :Union[str, Any]						=			SpeechTaTokenizer(UpperCAmelCase   )

											lowerCAmelCase   :Any						=			AddedToken('<mask>'     , lstrip=UpperCAmelCase     , rstrip=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			mask_token
											tokenizer.add_special_tokens({'mask_token': mask_token}   )
											tokenizer.add_tokens(['<ctc_blank>']   )

											tokenizer.save_pretrained(self.tmpdirname   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Any   )	->       Union[str, Any]:
											lowerCAmelCase   :Tuple						=			'this is a test'
											lowerCAmelCase   :Union[str, Any]						=			'this is a test'
											return input_text, output_text
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Any=False     , UpperCAmelCase							:	List[Any]=20     , UpperCAmelCase							:	str=5   )	->       Optional[Any]:
											lowerCAmelCase	, lowerCAmelCase   :Dict						=			self.get_input_output_texts(UpperCAmelCase   )
											lowerCAmelCase   :str						=			tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			tokenizer.decode(UpperCAmelCase     , clean_up_tokenization_spaces=UpperCAmelCase   )
											return text, ids
					def        UpperCAmelCase__ (							self							:	Any   )	->       Optional[Any]:
											lowerCAmelCase   :List[Any]						=			'<pad>'
											lowerCAmelCase   :Any						=			1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase   )     , UpperCAmelCase   )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase   )     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Any:
											lowerCAmelCase   :Dict						=			list(self.get_tokenizer().get_vocab().keys()   )

											self.assertEqual(vocab_keys[0]     , '<s>'   )
											self.assertEqual(vocab_keys[1]     , '<pad>'   )
											self.assertEqual(vocab_keys[-4]     , 'œ'   )
											self.assertEqual(vocab_keys[-2]     , '<mask>'   )
											self.assertEqual(vocab_keys[-1]     , '<ctc_blank>'   )
											self.assertEqual(len(UpperCAmelCase   )     , 81   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Union[str, Any]:
											self.assertEqual(self.get_tokenizer().vocab_size     , 79   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       List[str]:
											lowerCAmelCase   :Any						=			self.get_tokenizers(do_lower_case=UpperCAmelCase   )
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							lowerCAmelCase   :Optional[int]						=			tokenizer.vocab_size
																							lowerCAmelCase   :Union[str, Any]						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )

																							# We usually have added tokens from the start in tests because our vocab fixtures are
																							# smaller than the original vocabs - let's not assert this
																							# self.assertEqual(vocab_size, all_size)

																							lowerCAmelCase   :Optional[Any]						=			['aaaaa bbbbbb', 'cccccccccdddddddd']
																							lowerCAmelCase   :Union[str, Any]						=			tokenizer.add_tokens(UpperCAmelCase   )
																							lowerCAmelCase   :str						=			tokenizer.vocab_size
																							lowerCAmelCase   :List[Any]						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )
																							self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
																							self.assertEqual(UpperCAmelCase     , len(UpperCAmelCase   )   )
																							self.assertEqual(UpperCAmelCase     , all_size + len(UpperCAmelCase   )   )

																							lowerCAmelCase   :int						=			tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'     , add_special_tokens=UpperCAmelCase   )

																							self.assertGreaterEqual(len(UpperCAmelCase   )     , 4   )
																							self.assertGreater(tokens[0]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[-3]     , tokenizer.vocab_size - 1   )

																							lowerCAmelCase   :List[Any]						=			{'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
																							lowerCAmelCase   :str						=			tokenizer.add_special_tokens(UpperCAmelCase   )
																							lowerCAmelCase   :Optional[Any]						=			tokenizer.vocab_size
																							lowerCAmelCase   :Tuple						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )
																							self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
																							self.assertEqual(UpperCAmelCase     , len(UpperCAmelCase   )   )
																							self.assertEqual(UpperCAmelCase     , all_size_a + len(UpperCAmelCase   )   )

																							lowerCAmelCase   :List[str]						=			tokenizer.encode(
																							    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'     , add_special_tokens=UpperCAmelCase   )

																							self.assertGreaterEqual(len(UpperCAmelCase   )     , 6   )
																							self.assertGreater(tokens[0]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[0]     , tokens[1]   )
																							self.assertGreater(tokens[-3]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[-3]     , tokens[-4]   )
																							self.assertEqual(tokens[0]     , tokenizer.eos_token_id   )
																							self.assertEqual(tokens[-3]     , tokenizer.pad_token_id   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[Any]:
											pass
					def        UpperCAmelCase__ (							self							:	str   )	->       int:
											pass
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[Any]:
											lowerCAmelCase   :Optional[int]						=			self.get_tokenizer()

											lowerCAmelCase   :List[str]						=			tokenizer.tokenize('This is a test'   )
											# fmt: off
											self.assertListEqual(UpperCAmelCase     , [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']   )
											# fmt: on

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(UpperCAmelCase   )     , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]     , )

											lowerCAmelCase   :List[str]						=			tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
											self.assertListEqual(
											    UpperCAmelCase     , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']   )

											lowerCAmelCase   :Optional[int]						=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
											# fmt: off
											self.assertListEqual(UpperCAmelCase     , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]   )
											# fmt: on

											lowerCAmelCase   :int						=			tokenizer.convert_ids_to_tokens(UpperCAmelCase   )
											self.assertListEqual(
											    UpperCAmelCase     , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']   )
					@slow
					def        UpperCAmelCase__ (							self							:	str   )	->       Tuple:
											# Use custom sequence because this tokenizer does not handle numbers.
											lowerCAmelCase   :Any						=			[
											    'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
											    'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
											    'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
											    'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
											    'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
											    'conditioning on both left and right context in all layers.',
											    'The quick brown fox jumps over the lazy dog.',
											]

											# fmt: off
											lowerCAmelCase   :List[str]						=			{
											    'input_ids': [
											        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
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											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCAmelCase     , model_name='microsoft/speecht5_asr'     , revision='c5ef64c71905caeccde0e4462ef3f9077224c524'     , sequences=UpperCAmelCase     , )